Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Reduced Rank Regression
Authors: Qiong Wu, Felix MF Wong, Yanhua Li, Zhenming Liu, Varun Kanade
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our preliminary experiments con๏ฌrm that our algorithm often out-performs existing baselines, and is always at least competitive. |
| Researcher Affiliation | Collaboration | Qiong Wu William & Mary Felix M. F. Wong Independent Researcher Yanhua Li Worcester Polytechnic Institute Zhenming Liu William & Mary Varun Kanade University of Oxford. Correspondence to: Qiong Wu <EMAIL>. Currently at Google. |
| Pseudocode | Yes | Figure 1: Our algorithm (ADAPTIVE-RRR) for solving the regression y = Mx + ฯต. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes using a 'stock market dataset' and 'tweet data' but does not provide concrete access information (link, DOI, repository, or formal citation) for these datasets. |
| Dataset Splits | No | The paper refers to 'in-sample' and 'out-of-sample' data, implying a split, but does not specify the exact percentages or methodology for training, validation, and test splits. |
| Hardware Specification | No | The authors acknowledge William & Mary Research Computing for providing computational resources and technical support that have contributed to the results reported within this paper. This does not provide specific hardware models. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) used for the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for their model. |